cluster

29 Articles

It’s that time of year again, with the holidays fast approaching friends and family will be hounding you about what trinkets and shiny baubles they can pretend to surprise you with. Unfortunately there’s no person harder to shop for than the maker or hacker: if we want it, we’ve probably already built the thing. Or at least gotten it out of somebody else’s trash.

But if they absolutely, positively, simply have to buy you something that’s commercially made, then you could do worse than pointing them to this very slick Raspberry Pi cluster backplane from [miniNodes]. With the ability to support up to five of the often overlooked Pi Compute Modules, this little device will let you bring a punchy little ARM cluster online without having to build something from scratch.

The Compute Module is perfectly suited for clustering applications like this due to its much smaller size compared to the full-size Raspberry Pi, but we don’t see it get used that often because it needs to be jacked into an appropriate SODIMM connector. This makes it effectively useless for prototyping and quickly thrown together hacks (I.E. everything most people use the Pi for), and really only suitable for finished products and industrial applications. It’s really the line in the sand between playing around with the Pi and putting it to real work.

[miniNodes] calls their handy little device the Carrier Board, and beyond the obvious five SODIMM slots for the Pis to live in, there’s also an integrated gigabit switch with an uplink port to get them all connected to the network. The board powers all of the nodes through a single barrel connector on the side opposite the Ethernet jack, leaving behind the masses of spider’s web of USB cables we usually see with Pi clusters.

The board doesn’t come cheap at $259 USD, plus the five Pi Compute Modules which will set you back another $150. But for the ticket price you’ll have a 20 core ARM cluster with 5 GB of RAM and 20 GB of flash storage in a 200 x 100 millimeter (8 x 4 inch) footprint, with an energy consumption of under 20 watts when running at wide open throttle. This could be an excellent choice for mobile applications, or if you just want to experiment with parallel processing on a desktop-sized device.

In today’s world of over-the-air firmware upgrades in everything from cars to phones to refrigerators, it’s common for manufacturers of various things to lock out features in software and force you to pay for the upgrades. Even if the hardware is the same across all the models, you can still be on the hook if you want to unlock anything extra. And, it seems as though Suzuki might be following this trend as well, as [Sebastian] found out when he opened up his 2011 Vstrom motorcycle.

The main feature that was lacking on this bike was a gear indicator. Even though all the hardware was available in the gearbox, and the ECU was able to know the current gear in use, there was no indicator on the gauge cluster. By using an Arduino paired with an OBD reading tool (even motorcycles make use of OBD these days), [Sebastian] was able to wire an LED ring into the gauge cluster to show the current gear while he’s riding.

The build is very professionally done and is so well blended into the gauge cluster that even we had a hard time spotting it at first. While this feature might require some additional lighting on the gauge cluster for Suzuki to be able to offer this feature, we have seen other “missing” features in devices that could be unlocked with a laughably small amount of effort.

There seems to be a universal truth on the Internet: if you open up a service to the world, eventually somebody will come in and try to mess it up. If you have a comment section, trolls will come in and fill it with pedantic complaints (so we’ve heard anyway, naturally we have no experience with such matters). If you have a service where people can upload files, then it’s a guarantee that something unsavory is eventually going to take up residence on your server.

The nude-sniffing cluster is made up of a trio of Raspberry Pi computers, each with its own Movidius neural compute stick to perform the heavy lifting. [Christian] explains how he installed the compute stick SDK and Yahoo’s open source learning module for identifying questionable images, the aptly named open_nsfw. The system can be scaled up by adding more Pis to the system, and since it’s all ARM processors and compute sticks, it’s energy efficient enough the whole system can run off a 10 watt solar panel.

After opening up the system with a public web interface where users can scan their own images, he offered his system’s services to a large image hosting provider to see what it would find. Shockingly, the system was able to find over 3,000 images that contained suspected child pornography. The appropriate authorities were notified, and [Christian] encourages anyone else looking to search their servers for this kind of content to drop him a line. Truly hacking for good.

We’ve seen the supercomputer cluster work of [Nick Smith] from the UK before, but his latest build is quite lovely. This time around, he put together a 96-core supercomputer using the NanoPi Fire3, a Raspberry Pi alternative that has double the number of cores. His post takes you through how he built the supercomputer cluster, from designing the laser-cut acrylic case to routing the power cables.

If the computer you have isn’t particularly fast, there’s a well-documented way to get more out of it. You just need more of the same computer, and you can run your tasks on them all at the same time. Building computer clusters is an effective way of decreasing the time it takes for computers to solve certain problems, even if the computers themselves aren’t top-of-the-line hardware. Of course, with cheap enough hardware, people will build clusters out of just about anything, including the ESP32.

For this project, [Wei Lin] admits that this isn’t really a serious attempt at building speedy hardware, but rather an interesting exercise in creating a cluster as a sort of learning experience. ESP32 boards can be found for around $10 so building an experimental cluster with these is even more feasible than using the Raspberry Pi. [Wei Lin] goes into a great amount of detail on his GitHub page about all of his goals with the project, most of which involve exploring the functionality of the new cluster and its underpinnings.

While this might seem like little more than a thought experiment, it does have the advantage of being a great solution for problems that involve gathering data from points that are physically very far from one another. If you’ve ever been interested in parallel computing or computing clusters, this is a great project to check out. If you have more Raspberry Pis on hand than ESP32s and still want to build a cluster, check out this project that used a mere 750 of them for one.

It is getting difficult to find a desktop or laptop computer with only a single CPU. Even a typical ARM-based computer now probably has multiple cores. Of course, there’s nothing to stop you from using multiple microcontrollers — like the Arduino — together. To make the process neater, [Dimitris Platis] put together Moltoduino, essentially an Arduino on a shield made to plug into another Arduino. And, yes, they will stack. You can see a video about the open source boards, below.

The key is how the board brings the pins out to connections that are easy to jumper between boards. There are several obvious use cases, but one that [Dimitris] is particularly interested in is hardware-in-the-loop testing. The idea is that you can use a simulated I/O device in one computer to exchange fake data with the software under test.

Creating Raspberry Pi clusters is a popular hacker activity. Bitscope has been commercializing these clusters for a bit now and last year they created a cluster of 750 Pis for Los Alamos National Labs. You might wonder what an institution know for supercomputers wants with a cluster of Raspberry Pis. Turns out it is tough to justify taking a real high-speed cluster down just to test software. Now developers can run small test programs with a large number of CPU cores without requiring time on the big iron.

On the face of it, this doesn’t sound too hard, but hooking up 750 of anything is going to have its challenges. You have to provide power and carry away heat. They all have to communicate, and you aren’t going to want to house the thing in a few hundred square feet which makes heat and power even more difficult.